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Vision Computer Eyesight Gets a Lot More Accurate, NY Times Bits blog, August 18, 2014Computer Eyesight Gets a Lot More Accurate Building A Deeper Understanding of Images, Google Research Blog, September 5, 2014Building A Deeper Understanding of Images

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Games 1997: IBM’s Deep Blue defeats the reigning world chess champion Garry Kasparov 1996: Kasparov Beats Deep Blue “I could feel – I could smell – a new kind of intelligence across the table.” 1997: Deep Blue Beats Kasparov “Deep Blue hasn't proven anything.” 2007: Checkers is solvedCheckers is solved Though checkers programs had been beating the best human players for at least a decade before then 2014: Heads-up limit Texas Hold-em poker is solvedHeads-up limit Texas Hold-em poker is solved First game of imperfect information

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Mathematics In 1996, a computer program written by researchers at Argonne National Laboratory proved a mathematical conjecture unsolved for decades NY Times story: “[The proof] would have been called creative if a human had thought of it”NY Times story Mathematical software:

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Logistics, scheduling, planning During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people NASA’s Remote Agent software operated the Deep Space 1 spacecraft during two experiments in May 1999Remote Agent In 2004, NASA introduced the MAPGEN system to plan the daily operations for the Mars Exploration RoversMAPGEN

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Herbert Simon, 1957 “It is not my aim to surprise or shock you – but … there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until – in a visible future – the range of problems they can handle will be coextensive with the range to which human mind has been applied. More precisely: within 10 years a computer would be chess champion, and an important new mathematical theorem would be proved by a computer.” Prediction came true – but 40 years later instead of 10

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Harder than originally thought 1966: Eliza chatbot (Weizenbaum)Eliza “ … mother …” → “Tell me more about your family” “I wanted to adopt a puppy, but it’s too young to be separated from its mother.” 1954: Georgetown-IBM experimentGeorgetown-IBM experiment Completely automatic translation of more than sixty Russian sentences into English Only six grammar rules, 250 vocabulary words, restricted to organic chemistry Promised that machine translation would be solved in three to five years (press release)press release Automatic Language Processing Advisory Committee (ALPAC) report (1966): machine translation has failed “The spirit is willing but the flesh is weak.” → “The vodka is strong but the meat is rotten.”

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History of AI: Taste of failure 1940s First model of a neuron (W. S. McCulloch & W. Pitts) Hebbian learning rule Cybernetics 1950sTuring Test Perceptrons (F. Rosenblatt) Computer chess and checkers (C. Shannon, A. Samuel) Machine translation (Georgetown-IBM experiment) Theorem provers (A. Newell and H. Simon, H. Gelernter and N. Rochester) Late 1960sMachine translation deemed a failure Neural nets deprecated (M. Minsky and S. Papert, 1969)* Early 1970s Intractability is recognized as a fundamental problem Late 1970sThe first “AI Winter”“AI Winter” * A sociological study of the official history of the perceptrons controversy A sociological study of the official history of the perceptrons controversy

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History of AI to the present day 1980sExpert systems boom Late 1980s- Expert system bust; the second “AI winter” Early 1990s Mid-1980s Neural networks and back-propagation Late 1980sProbabilistic reasoning on the ascent 1990s-PresentMachine learning everywhere Big Data Deep Learning Building Smarter Machines: NY Times Timeline AAAI Timeline History of AI on Wikipedia

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What accounts for recent successes in AI? Faster computers The IBM 704 vacuum tube machine that played chess in 1958 could do about 50,000 calculations per second Deep Blue could do 50 billion calculations per second – a million times faster! Dominance of statistical approaches, machine learning Big data Crowdsourcing

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Historical themes Boom and bust cycles Periods of (unjustified) optimism followed by periods of disillusionment and reduced funding Silver bulletism (Levesque, 2013):Levesque, 2013 “The tendency to believe in a silver bullet for AI, coupled with the belief that previous beliefs about silver bullets were hopelessly naïve” Image problems AI effect: As soon as a machine gets good at performing some task, the task is no longer considered to require much intelligenceAI effect AI as a threat?

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Historical themes Moravec’s paradox “It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one- year-old when it comes to perception and mobility” [Hans Moravec, 1988] Why is this? Early AI researchers concentrated on the tasks that they themselves found the most challenging, abilities of animals and two-year-olds were overlooked We are least conscious of what our brain does best Sensorimotor skills took millions of years to evolve, whereas abstract thinking is a relatively recent development

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Two brain systems? System 1: fast, automatic, subconscious, emotional Detect hostility on a face or in a voice Orient to the source of a sudden sound Answer to 2+2=? Read words on large billboards Drive on an empty road System 2: slow, effortful, logical, calculating, conscious Focus on the voice of a particular person in a crowded and noisy room Search memory to identify a melody Count the occurrences of the letter a on a page Compare two washing machines for overall value Fill out a tax form Check the validity of a complex logical argument

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In this class Part 1: sequential reasoning Part 2: pattern recognition and learning

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Philosophy of this class Goal: use machines to solve hard problems that are traditionally thought to require human intelligence We will try to follow a sound scientific/engineering methodology Consider relatively limited application domains Use well-defined input/output specifications Define operational criteria amenable to objective validation Zero in on essential problem features Focus on principles and basic building blocks